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Abstract：
BACKGROUND: Colorectal cancer (CRC) is a common malignant tumor, and liver metastasis is one of the main recurrence and metastasis modes that seriously affect patients\' survival rate and quality of life. Indicators such as albumin bilirubin (ALBI) score, liver function index, and carcinoembryonic antigen (CEA) have shown some potential in the prediction of liver metastasis but have not been fully explored.
OBJECTIVE: To evaluate its predictive value for liver metastasis of CRC by conducting the combined analysis of ALBI, liver function index, and CEA, and to provide a more accurate liver metastasis risk assessment tool for clinical treatment guidance.
METHODS: This study retrospectively analyzed the clinical data of patients with CRC who received surgical treatment in our hospital from January 2018 to July 2023 and were followed up for 24 months. According to the follow-up results, the enrolled patients were divided into a liver metastasis group and a nonliver metastasis group and randomly divided into a modeling group and a verification group at a ratio of 2:1. The risk factors for liver metastasis in patients with CRC were analyzed, a prediction model was constructed by least absolute shrinkage and selection operator (LASSO) logistic regression, internal validation was performed by the bootstrap method, the reliability of the prediction model was evaluated by subject-work characteristic curves, calibration curves, and clinical decision curves, and a column graph was drawn to show the prediction results.
RESULTS: Of 130 patients were enrolled in the modeling group and 65 patients were enrolled in the verification group out of the 195 patients with CRC who fulfilled the inclusion and exclusion criteria. Through LASSO regression variable screening and logistic regression analysis. The ALBI score, alanine aminotransferase (ALT), and CEA were found to be independent predictors of liver metastases in CRC patients [odds ratio (OR) = 8.062, 95% confidence interval (CI): 2.545-25.540], (OR = 1.037, 95%CI: 1.004-1.071) and (OR = 1.025, 95%CI: 1.008-1.043). The area under the receiver operating characteristic curve (AUC) for the combined prediction of CRLM in the modeling group was 0.921, with a sensitivity of 78.0% and a specificity of 95.0%. The H-index was 0.921, and the H-L fit curve had χ2 = 0.851, a P value of 0.654, and a slope of the calibration curve approaching 1. This indicates that the model is extremely accurate, and the clinical decision curve demonstrates that it can be applied effectively in the real world. We conducted internal verification of one thousand resamplings of the modeling group data using the bootstrap method. The AUC was 0.913, while the accuracy was 0.869 and the kappa consistency was 0.709. The combination prediction of liver metastasis in patients with CRC in the verification group had an AUC of 0.918, sensitivity of 85.0%, specificity of 95.6%, C-index of 0.918, and an H-L fitting curve with χ 2 = 0.586, P = 0.746.
CONCLUSIONS: The ALBI score, ALT level, and CEA level have a certain value in predicting liver metastasis in patients with CRC. These three criteria exhibit a high level of efficacy in forecasting liver metastases in patients diagnosed with CRC. The risk prediction model developed in this work shows great potential for practical application.
OBJECTIVE: To evaluate its predictive value for liver metastasis of CRC by conducting the combined analysis of ALBI, liver function index, and CEA, and to provide a more accurate liver metastasis risk assessment tool for clinical treatment guidance.
METHODS: This study retrospectively analyzed the clinical data of patients with CRC who received surgical treatment in our hospital from January 2018 to July 2023 and were followed up for 24 months. According to the follow-up results, the enrolled patients were divided into a liver metastasis group and a nonliver metastasis group and randomly divided into a modeling group and a verification group at a ratio of 2:1. The risk factors for liver metastasis in patients with CRC were analyzed, a prediction model was constructed by least absolute shrinkage and selection operator (LASSO) logistic regression, internal validation was performed by the bootstrap method, the reliability of the prediction model was evaluated by subject-work characteristic curves, calibration curves, and clinical decision curves, and a column graph was drawn to show the prediction results.
RESULTS: Of 130 patients were enrolled in the modeling group and 65 patients were enrolled in the verification group out of the 195 patients with CRC who fulfilled the inclusion and exclusion criteria. Through LASSO regression variable screening and logistic regression analysis. The ALBI score, alanine aminotransferase (ALT), and CEA were found to be independent predictors of liver metastases in CRC patients [odds ratio (OR) = 8.062, 95% confidence interval (CI): 2.545-25.540], (OR = 1.037, 95%CI: 1.004-1.071) and (OR = 1.025, 95%CI: 1.008-1.043). The area under the receiver operating characteristic curve (AUC) for the combined prediction of CRLM in the modeling group was 0.921, with a sensitivity of 78.0% and a specificity of 95.0%. The H-index was 0.921, and the H-L fit curve had χ2 = 0.851, a P value of 0.654, and a slope of the calibration curve approaching 1. This indicates that the model is extremely accurate, and the clinical decision curve demonstrates that it can be applied effectively in the real world. We conducted internal verification of one thousand resamplings of the modeling group data using the bootstrap method. The AUC was 0.913, while the accuracy was 0.869 and the kappa consistency was 0.709. The combination prediction of liver metastasis in patients with CRC in the verification group had an AUC of 0.918, sensitivity of 85.0%, specificity of 95.6%, C-index of 0.918, and an H-L fitting curve with χ 2 = 0.586, P = 0.746.
CONCLUSIONS: The ALBI score, ALT level, and CEA level have a certain value in predicting liver metastasis in patients with CRC. These three criteria exhibit a high level of efficacy in forecasting liver metastases in patients diagnosed with CRC. The risk prediction model developed in this work shows great potential for practical application.
摘要：
背景：结直肠癌（CRC）是一种常见的恶性肿瘤，肝转移是严重影响患者生存率和生活质量的主要复发转移方式之一。白蛋白胆红素（ALBI）评分等指标，肝功能指标,和癌胚抗原（CEA）在预测肝转移方面显示出一定的潜力，但尚未得到充分的探索。
目的：通过ALBI联合分析评价其对结直肠癌肝转移的预测价值。肝功能指标,CEA,为临床治疗提供更准确的肝转移风险评估工具。
方法：本研究回顾性分析2018年1月至2023年7月在我院接受手术治疗的CRC患者的临床资料，随访24个月。根据后续结果,将纳入的患者分为肝转移组和非肝转移组,并以2:1的比例随机分为模型组和验证组.分析结直肠癌患者肝转移的危险因素,通过最小绝对收缩和选择算子(LASSO)逻辑回归构建了预测模型，内部验证是通过引导方法进行的，通过受试者-工作特征曲线评估预测模型的可靠性，校正曲线,和临床决策曲线，并绘制了柱状图来显示预测结果。
结果：在符合纳入和排除标准的195例CRC患者中，有130例患者纳入模型组，65例患者纳入验证组。通过LASSO回归变量筛选和logistic回归分析。ALBI得分,丙氨酸氨基转移酶（ALT），发现CEA是CRC患者肝转移的独立预测因子[比值比（OR）=8.062，95％置信区间（CI）：2.545-25.540]，（OR=1.037，95CI：1.004-1.071）和（OR=1.025，95CI：1.008-1.043）。模型组联合预测CRLM的受试者工作特征曲线下面积（AUC）为0.921，灵敏度为78.0％，特异性为95.0％。H指数为0.921，H-L拟合曲线的χ2=0.851，P值为0.654，校准曲线的斜率接近1。这表明该模型非常准确，和临床决策曲线表明，它可以有效地应用在现实世界中。我们使用Bootstrap方法对建模组数据的一千个重采样进行了内部验证。AUC为0.913，而准确性为0.869，κ一致性为0.709。验证组联合预测CRC患者肝转移的AUC为0.918,敏感性为85.0%,特异性为95.6%，C指数为0.918，H-L拟合曲线χ2=0.586，P=0.746。
结论：ALBI评分，ALT水平,CEA水平对预测结直肠癌患者肝转移有一定的价值。这三个标准在预测诊断为CRC的患者的肝转移方面表现出高水平的功效。这项工作开发的风险预测模型显示出巨大的实际应用潜力。
目的：通过ALBI联合分析评价其对结直肠癌肝转移的预测价值。肝功能指标,CEA,为临床治疗提供更准确的肝转移风险评估工具。
方法：本研究回顾性分析2018年1月至2023年7月在我院接受手术治疗的CRC患者的临床资料，随访24个月。根据后续结果,将纳入的患者分为肝转移组和非肝转移组,并以2:1的比例随机分为模型组和验证组.分析结直肠癌患者肝转移的危险因素,通过最小绝对收缩和选择算子(LASSO)逻辑回归构建了预测模型，内部验证是通过引导方法进行的，通过受试者-工作特征曲线评估预测模型的可靠性，校正曲线,和临床决策曲线，并绘制了柱状图来显示预测结果。
结果：在符合纳入和排除标准的195例CRC患者中，有130例患者纳入模型组，65例患者纳入验证组。通过LASSO回归变量筛选和logistic回归分析。ALBI得分,丙氨酸氨基转移酶（ALT），发现CEA是CRC患者肝转移的独立预测因子[比值比（OR）=8.062，95％置信区间（CI）：2.545-25.540]，（OR=1.037，95CI：1.004-1.071）和（OR=1.025，95CI：1.008-1.043）。模型组联合预测CRLM的受试者工作特征曲线下面积（AUC）为0.921，灵敏度为78.0％，特异性为95.0％。H指数为0.921，H-L拟合曲线的χ2=0.851，P值为0.654，校准曲线的斜率接近1。这表明该模型非常准确，和临床决策曲线表明，它可以有效地应用在现实世界中。我们使用Bootstrap方法对建模组数据的一千个重采样进行了内部验证。AUC为0.913，而准确性为0.869，κ一致性为0.709。验证组联合预测CRC患者肝转移的AUC为0.918,敏感性为85.0%,特异性为95.6%，C指数为0.918，H-L拟合曲线χ2=0.586，P=0.746。
结论：ALBI评分，ALT水平,CEA水平对预测结直肠癌患者肝转移有一定的价值。这三个标准在预测诊断为CRC的患者的肝转移方面表现出高水平的功效。这项工作开发的风险预测模型显示出巨大的实际应用潜力。
